apply Black 25.11.0 style in fbcode (70/92)

Summary:
Formats the covered files with pyfmt.

paintitblack

Reviewed By: itamaro

Differential Revision: D90476295

fbshipit-source-id: 5101d4aae980a9f8955a4cb10bae23997c48837f

Author: bowiechen

examples/albef/data/transforms.py

@@ -8,7 +8,6 @@
 from typing import List, Tuple, Union
 
 import torch
-
 from torchtext.transforms import PadTransform, Sequential, ToTensor, Truncate
 from torchvision import transforms
 from transformers.models.bert.tokenization_bert import BertTokenizer

examples/albef/data/vqa_dataset.py

@@ -9,7 +9,6 @@
 from typing import Callable, List, Tuple, Union
 
 import torch
-
 from PIL import Image
 from torch import Tensor
 from torch.utils.data import Dataset

examples/albef/finetune_vqa.py

@@ -18,7 +18,6 @@
 from model import albef_model_for_vqa
 from torch.optim import AdamW
 from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts
-
 from utils import (
     add_weight_decay,
     get_rank,

examples/albef/model.py

@@ -572,15 +572,15 @@ def _eval_forward(
             return self._encode_image(image)
 
         elif input_type == "text":
-            assert (
-                text is not None and text_atts is not None
-            ), "text and text attention mask cannot be None"
+            assert text is not None and text_atts is not None, (
+                "text and text attention mask cannot be None"
+            )
             return self._encode_text(text, text_atts)
 
         elif input_type == "multimodal":
-            assert (
-                image is not None and text is not None and text_atts is not None
-            ), "image embeddings, text embeddings, and text attention mask cannot be None"
+            assert image is not None and text is not None and text_atts is not None, (
+                "image embeddings, text embeddings, and text attention mask cannot be None"
+            )
             return self._image_text_matching_score(image, text, text_atts)
 
         else:

examples/cnn_lstm/cnn_encoder.py

@@ -36,9 +36,9 @@ def __init__(
         assert len(input_dims) == len(output_dims) and len(output_dims) == len(
             kernel_sizes
         ), "input_dims, output_dims, and kernel_sizes should all have the same length"
-        assert (
-            input_dims[1:] == output_dims[:-1]
-        ), "output_dims should match input_dims offset by one"
+        assert input_dims[1:] == output_dims[:-1], (
+            "output_dims should match input_dims offset by one"
+        )
         for in_channels, out_channels, kernel_size in zip(
             input_dims,
             output_dims,

examples/cnn_lstm/cnn_lstm.py

@@ -8,7 +8,6 @@
 
 from examples.cnn_lstm.cnn_encoder import CNNEncoder
 from examples.cnn_lstm.lstm_encoder import LSTMEncoder
-
 from torch import nn
 from torchmultimodal.models.late_fusion import LateFusion
 from torchmultimodal.modules.fusions.concat_fusion import ConcatFusionModule

examples/cnn_lstm/tests/test_cnn_encoder.py

@@ -5,7 +5,6 @@
 # LICENSE file in the root directory of this source tree.
 
 import pytest
-
 import torch
 from examples.cnn_lstm.cnn_encoder import CNNEncoder
 from tests.test_utils import assert_expected, set_rng_seed

examples/cnn_lstm/tests/test_cnn_lstm.py

@@ -5,7 +5,6 @@
 # LICENSE file in the root directory of this source tree.
 
 import pytest
-
 import torch
 from examples.cnn_lstm.cnn_lstm import cnn_lstm_classifier
 from tests.test_utils import assert_expected, set_rng_seed

examples/cnn_lstm/tests/test_lstm_encoder.py

@@ -5,7 +5,6 @@
 # LICENSE file in the root directory of this source tree.
 
 import pytest
-
 import torch
 from examples.cnn_lstm.lstm_encoder import LSTMEncoder
 from tests.test_utils import assert_expected

examples/flava/data/datamodules.py

@@ -467,9 +467,9 @@ def __init__(
     def _parse_info(
         self, info: TorchVisionDatasetInfo, dataset_root: Optional[str] = None
     ):
-        assert hasattr(
-            torchvision.datasets, info.key
-        ), f"No dataset named {info.key} present in torchvision.datasets"
+        assert hasattr(torchvision.datasets, info.key), (
+            f"No dataset named {info.key} present in torchvision.datasets"
+        )
         class_ptr = getattr(torchvision.datasets, info.key)
         if dataset_root is None:
             dataset_root = os.path.join(TRANSFORMERS_CACHE, "datasets", "torchvision")